Attempt to use convolutional neural network to achieve kinematic analysis of
plane bar structure. Through 3dsMax animation software and OpenCV module,
self-build image dataset of geometrically stable system and geometrically
unstable system. we construct and train convolutional neural network model
based on the TensorFlow and Keras deep learning platform framework. The model
achieves 100% accuracy on the training set, validation set, and test set. The
accuracy on the additional test set is 93.7%, indicating that convolutional
neural network can learn and master the relevant knowledge of kinematic
analysis of structural mechanics. In the future, the generalization ability of
the model can be improved through the diversity of dataset, which has the
potential to surpass human experts for complex structures. Convolutional neural
network has certain practical value in the field of kinematic analysis of
structural mechanics. Using visualization technology, we reveal how
convolutional neural network learns and recognizes structural features. Using
pre-trained VGG16 model for feature extraction and fine-tuning, we found that
the generalization ability is inferior to the self-built model.

尝试使用卷积神经网络实现平面杆结构的运动学分析。通过 3dsMax 动画软件和 OpenCV 模块，构建了几何稳定系统和几何不稳定系统的图像数据集。基于 TensorFlow 和 Keras 深度学习平台框架，构建和训练了卷积神经网络模型。模型在训练集、验证集和测试集上均达到了 100% 的准确率。附加测试集上的准确率为 93.7%，显示出卷积神经网络能够学习和掌握结构力学运动学分析相关知识。通过多样化数据集，未来可以提高模型的泛化能力，有潜力在复杂结构领域超越人类专家。卷积神经网络在结构力学运动学分析领域具有一定的实际价值。利用可视化技术，揭示了卷积神经网络学习和识别结构特征的过程。使用预训练的 VGG16 模型进行特征提取和微调后，发现其泛化能力不如自建模型。